Bitcoin Treasury Risk Management: Using Ollama for Volatility Analysis and Hedging Strategies

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a model optimized for financial analysis
ollama pull llama2:13b

# Verify installation
ollama list

import pandas as pd
import numpy as np
import requests
import json
from datetime import datetime, timedelta
import ollama

# Configure Ollama client
client = ollama.Client()

def setup_bitcoin_data_pipeline():
    """Initialize Bitcoin price data pipeline"""
    # CoinGecko API for historical data
    base_url = "https://api.coingecko.com/api/v3"
    
    # Configure data parameters
    params = {
        'vs_currency': 'usd',
        'days': '365',  # One year of data
        'interval': 'daily'
    }
    
    return base_url, params

def calculate_bitcoin_volatility(price_data, window=30):
    """Calculate rolling volatility metrics"""
    
    # Calculate daily returns
    returns = price_data['price'].pct_change().dropna()
    
    # Rolling volatility (annualized)
    rolling_vol = returns.rolling(window=window).std() * np.sqrt(365)
    
    # GARCH volatility estimation
    from arch import arch_model
    garch_model = arch_model(returns * 100, vol='Garch', p=1, q=1)
    garch_fit = garch_model.fit(disp='off')
    
    # Prepare data for Ollama analysis
    volatility_summary = {
        'current_vol': rolling_vol.iloc[-1],
        'avg_vol': rolling_vol.mean(),
        'vol_trend': 'increasing' if rolling_vol.iloc[-1] > rolling_vol.mean() else 'decreasing',
        'garch_forecast': garch_fit.forecast(horizon=5).variance.iloc[-1, :].mean()
    }
    
    return volatility_summary, rolling_vol

def analyze_risk_with_ollama(volatility_data, market_context):
    """Generate AI-powered risk assessment"""
    
    prompt = f"""
    Analyze this Bitcoin volatility data for treasury risk management:
    
    Current Volatility: {volatility_data['current_vol']:.2%}
    Average Volatility: {volatility_data['avg_vol']:.2%}
    Trend: {volatility_data['vol_trend']}
    GARCH Forecast: {volatility_data['garch_forecast']:.2%}
    
    Market Context: {market_context}
    
    Provide:
    1. Risk level assessment (Low/Medium/High)
    2. Key risk factors
    3. Recommended hedging actions
    4. Timeline for reassessment
    
    Format as structured JSON.
    """
    
    response = client.generate(
        model='llama2:13b',
        prompt=prompt,
        format='json'
    )
    
    return json.loads(response['response'])

def calculate_options_hedge_sizing(portfolio_value, risk_tolerance, volatility):
    """Calculate optimal options hedge ratios"""
    
    # Target hedge ratio based on risk tolerance
    target_hedge_ratio = min(0.8, risk_tolerance * 1.5)
    
    # Adjust for current volatility
    vol_adjustment = min(1.5, volatility / 0.6)  # Base volatility of 60%
    adjusted_ratio = target_hedge_ratio * vol_adjustment
    
    # Calculate put option requirements
    put_coverage = portfolio_value * adjusted_ratio
    
    # Strike price selection (5-10% out of the money)
    current_price = get_current_bitcoin_price()
    put_strike = current_price * 0.9  # 10% OTM
    
    hedge_plan = {
        'hedge_ratio': adjusted_ratio,
        'put_coverage': put_coverage,
        'put_strike': put_strike,
        'estimated_cost': put_coverage * 0.03  # Approximate premium
    }
    
    return hedge_plan

def execute_dynamic_rebalancing(current_position, target_allocation, market_conditions):
    """Implement dynamic hedging rebalancing"""
    
    # Calculate required adjustments
    position_diff = target_allocation - current_position
    
    # Market condition adjustments
    if market_conditions['volatility'] > 0.8:  # High volatility
        adjustment_speed = 0.5  # Slower adjustments
    else:
        adjustment_speed = 1.0  # Full adjustments
    
    # Execute gradual rebalancing
    rebalance_amount = position_diff * adjustment_speed
    
    # Generate trade instructions
    trade_plan = {
        'action': 'buy' if rebalance_amount > 0 else 'sell',
        'amount': abs(rebalance_amount),
        'execution_style': 'TWAP',  # Time-weighted average price
        'timeframe': '4_hours'
    }
    
    return trade_plan

def create_risk_monitoring_system():
    """Setup comprehensive risk monitoring"""
    
    risk_thresholds = {
        'volatility_spike': 1.0,  # 100% annualized
        'correlation_break': 0.3,  # Major correlation shift
        'drawdown_limit': 0.15,   # 15% maximum drawdown
        'var_breach': 0.05        # 5% VaR confidence level
    }
    
    def check_risk_thresholds(current_metrics):
        alerts = []
        
        for metric, threshold in risk_thresholds.items():
            if current_metrics.get(metric, 0) > threshold:
                alerts.append({
                    'metric': metric,
                    'current_value': current_metrics[metric],
                    'threshold': threshold,
                    'severity': 'high' if current_metrics[metric] > threshold * 1.5 else 'medium'
                })
        
        return alerts
    
    return check_risk_thresholds

def generate_intelligent_alerts(risk_metrics, historical_context):
    """Create context-aware risk alerts"""
    
    alert_prompt = f"""
    Bitcoin treasury risk alert analysis:
    
    Current Metrics:
    - Volatility: {risk_metrics['volatility']:.2%}
    - 1-Day VaR: {risk_metrics['var_1d']:.2%}
    - Max Drawdown: {risk_metrics['max_drawdown']:.2%}
    - Correlation with S&P 500: {risk_metrics['sp500_correlation']:.2f}
    
    Historical Context: {historical_context}
    
    Generate alert message with:
    1. Urgency level (Low/Medium/High/Critical)
    2. Primary concern
    3. Recommended immediate actions
    4. Context for decision makers
    
    Keep under 200 words, executive-friendly language.
    """
    
    alert_response = client.generate(
        model='llama2:13b',
        prompt=alert_prompt
    )
    
    return alert_response['response']